For bounded effect sizes
data_effectsizes |>
filter(type %in% c("peta2", "r", "abs_r", "rho", "abs_rho", "R2", "OR")) |>
group_by(type) |>
summarize(n = n(),
percent_impossible = round_half_up(mean(!possible)*100, 3),
n_impossible = sum(!possible)) |>
arrange(desc(percent_impossible)) |>
kable() |>
kable_classic(full_width = FALSE)| type | n | percent_impossible | n_impossible |
|---|---|---|---|
| R2 | 8160 | 5.723 | 467 |
| abs_r | 131669 | 0.774 | 1019 |
| r | 131669 | 0.774 | 1019 |
| abs_rho | 3149 | 0.445 | 14 |
| rho | 3149 | 0.445 | 14 |
| OR | 43890 | 0.011 | 5 |
| peta2 | 1219322 | 0.002 | 29 |
data_effectsizes_possible |>
distinct(doi, .keep_all = TRUE) |>
count() |>
kable() |>
kable_classic(full_width = FALSE)| n |
|---|
| 173926 |
data_effectsizes_possible |>
distinct(doi, .keep_all = TRUE) |>
count(subfield) |>
kable() |>
kable_classic(full_width = FALSE)| subfield | n |
|---|---|
| Applied Psychology | 20262 |
| Clinical Psychology | 28944 |
| Developmental and Educational Psychology | 51949 |
| Experimental and Cognitive Psychology | 12662 |
| General Psychology | 46084 |
| Social Psychology | 14025 |
# data_effectsizes_possible |>
# distinct(doi, .keep_all = TRUE) |>
# count(journal) |>
# kable() |>
# kable_classic(full_width = FALSE)
data_effectsizes_possible |>
distinct(journal) |>
count() |>
kable() |>
kable_classic(full_width = FALSE)| n |
|---|
| 386 |
data_effectsizes_possible |>
distinct(year) |>
arrange(desc(year)) |>
slice(1, n()) |>
kable() |>
kable_classic(full_width = FALSE)| year |
|---|
| 2024 |
| 2004 |
data_effectsizes_possible |>
count(type) |>
arrange(desc(n)) |>
kable() |>
kable_classic(full_width = FALSE)| type | n |
|---|---|
| peta2 | 1219293 |
| d_s | 454687 |
| d_z | 454687 |
| chi2 | 185020 |
| abs_stdB | 143581 |
| stdB | 143581 |
| abs_r | 130650 |
| r | 130650 |
| B | 104679 |
| OR | 43885 |
| logOR | 43885 |
| abs_OR | 43868 |
| d_native | 23089 |
| sqrtR2 | 8150 |
| R2 | 7693 |
| Wald | 5941 |
| abs_rho | 3135 |
| rho | 3135 |
data_percentiles_long <- data_effectsizes_possible |>
filter(type %in% unique(type)) |>
group_by(type) |>
summarise(
across(
.cols = everything(),
.fns = list,
.names = "{.col}_list"
), # just for clarity — we only care about estimate column
.groups = "drop_last"
) |>
select(type, estimate = estimate_list) |>
unnest(estimate) |>
group_by(type) |>
summarise(
percentile = c(1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) / 100,
value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
.groups = "drop"
) |>
mutate(percentile = percentile * 100)
data_percentiles <- data_percentiles_long |>
pivot_wider(names_from = type, values_from = value) |>
select(percentile,
d_native,
d_s,
d_z,
abs_r,
#r,
#rho,
#abs_rho,
#sqrtR2,
peta2,
R2,
#OR,
abs_OR,
#logOR,
chi2,
B,
#abs_B,
stdB,
abs_stdB)
#Walddata_percentiles |>
mutate_if(is.numeric, round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| percentile | d_native | d_s | d_z | abs_r | peta2 | R2 | abs_OR | chi2 | B | stdB | abs_stdB |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.02 | 0.02 | 0.01 | 0.01 | 0.00 | 0.00 | 1.01 | 0.03 | -1.36 | -0.42 | 0.00 |
| 5 | 0.07 | 0.09 | 0.04 | 0.06 | 0.00 | 0.01 | 1.03 | 0.57 | -0.01 | 0.00 | 0.02 |
| 10 | 0.13 | 0.17 | 0.08 | 0.11 | 0.01 | 0.01 | 1.08 | 1.60 | 0.01 | 0.03 | 0.05 |
| 20 | 0.24 | 0.30 | 0.15 | 0.17 | 0.03 | 0.03 | 1.19 | 4.29 | 0.06 | 0.08 | 0.10 |
| 25 | 0.29 | 0.36 | 0.18 | 0.20 | 0.04 | 0.04 | 1.27 | 5.40 | 0.08 | 0.10 | 0.12 |
| 30 | 0.34 | 0.42 | 0.21 | 0.22 | 0.05 | 0.06 | 1.34 | 6.70 | 0.11 | 0.13 | 0.14 |
| 40 | 0.45 | 0.55 | 0.28 | 0.27 | 0.08 | 0.09 | 1.53 | 10.16 | 0.18 | 0.17 | 0.18 |
| 50 | 0.56 | 0.71 | 0.36 | 0.33 | 0.11 | 0.13 | 1.76 | 15.97 | 0.27 | 0.22 | 0.23 |
| 60 | 0.70 | 0.90 | 0.46 | 0.38 | 0.16 | 0.17 | 2.05 | 27.16 | 0.40 | 0.28 | 0.29 |
| 70 | 0.89 | 1.17 | 0.59 | 0.45 | 0.24 | 0.24 | 2.49 | 53.12 | 0.61 | 0.35 | 0.36 |
| 75 | 1.01 | 1.34 | 0.68 | 0.49 | 0.29 | 0.28 | 2.80 | 79.98 | 0.79 | 0.41 | 0.42 |
| 80 | 1.14 | 1.57 | 0.80 | 0.54 | 0.36 | 0.33 | 3.25 | 129.78 | 1.09 | 0.47 | 0.49 |
| 90 | 1.61 | 2.40 | 1.23 | 0.67 | 0.55 | 0.48 | 5.26 | 441.13 | 2.51 | 0.75 | 0.78 |
| 95 | 2.30 | 3.40 | 1.74 | 0.78 | 0.71 | 0.62 | 8.85 | 986.36 | 5.62 | 1.59 | 1.73 |
| 99 | 7.14 | 7.48 | 3.87 | 0.94 | 0.91 | 0.89 | 33.33 | 4247.75 | 32.24 | 12.41 | 14.65 |
# data_percentiles |>
# pivot_longer(col = -percentile,
# names_to = "estimator",
# values_to = "estimate") |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_wrap(~ estimator, scales = "free")
#
# data_percentiles |>
# pivot_longer(col = -percentile,
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_wrap(~ estimator, scales = "free")data_percentiles_for_plot <- data_percentiles_long |>
filter(type %in% c("d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"abs_stdB",
"chi2")) |>
mutate(type_lab = case_when(
type == "d_native" ~ "\"Cohen's\"~'|'*italic(d)*'|'~'(reported)'",
type == "d_s" ~ "\"Cohen's\"~'|'*italic(d)[s]*'|'~'(from t-test)'",
type == "d_z" ~ "\"Cohen's\"~'|'*italic(d)[z]*'|'~'(from t-test)'",
type == "abs_r" ~ "\"Pearson's\"~'|'*italic(r)*'|'",
type == "peta2" ~ "eta[p]^2",
type == "R2" ~ "italic(R)^2",
type == "abs_OR" ~ "\"|Odds Ratio|\"",
type == "abs_stdB" ~ "'|'*italic(beta)*'|'",
type == "chi2" ~ "chi^2",
TRUE ~ type
)) |>
mutate(type_lab = fct_relevel(
type_lab,
"\"Cohen's\"~'|'*italic(d)*'|'~'(reported)'",
"\"Cohen's\"~'|'*italic(d)[s]*'|'~'(from t-test)'",
"\"Cohen's\"~'|'*italic(d)[z]*'|'~'(from t-test)'",
"\"Pearson's\"~'|'*italic(r)*'|'",
"eta[p]^2",
"italic(R)^2",
"\"|Odds Ratio|\"",
"'|'*italic(beta)*'|'",
"chi^2"
)) |>
drop_na() labs_map <- c(
d_native = "\"Cohen's\"~group('|',italic(d),'|')~'(reported)'",
d_s = "\"Independent Cohen's\"~group('|',italic(d)[s],'|')~'(recalculated from t-test)'",
d_z = "\"Dependent Cohen's\"~group('|',italic(d)[z],'|')~'(recalculated from t-test)'"
)
label_map_parsed <- function(map) {
force(map)
function(x) parse(text = unname(map[x]))
}
data_percentiles_for_plot |>
filter(percentile < 99) |>
filter(str_detect(type, "d_")) |>
mutate(type = fct_relevel(type, "d_z", "d_native", "d_s")) |>
ggplot(aes(value, percentile, color = type)) +
geom_line() +
geom_point() +
scale_y_continuous(
#breaks = c(1, seq(5, 95, 5), 99),
breaks = c(1, seq(5, 95, 5)),
name = "Percentile"
) +
scale_x_continuous(
breaks = scales::breaks_pretty(n = 8),
#limits = c(0, 1),
name = expression("Cohen's" ~ group("|", italic(d), "|"))
) +
theme_linedraw() +
theme(
panel.grid.minor = element_blank(),
strip.placement = "outside",
strip.background = element_blank(), # no fill or box
strip.text = element_text(colour = "black"),
legend.position = c(0.65, 0.3)
) +
scale_colour_discrete(
name = "Method",
breaks = c("d_z", "d_native", "d_s"), # desired legend order
#breaks = names(labs_map),
labels = label_map_parsed(labs_map)
)data_percentiles_for_plot |>
filter(percentile < 99) |>
filter(!str_detect(type, "d_")) |>
ggplot(aes(value, percentile)) +
geom_line() +
geom_point() +
scale_y_continuous(
#breaks = c(1, seq(5, 95, 5), 99),
breaks = c(1, seq(5, 95, 5)),
name = "Percentile"
) +
scale_x_continuous(
breaks = scales::breaks_pretty(n = 8),
#limits = c(0, 1),
name = "Effect size"
) +
theme_linedraw() +
theme(
panel.grid.minor = element_blank(),
strip.placement = "outside",
strip.background = element_blank(), # no fill or box
strip.text = element_text(colour = "black")
#legend.position = c(0.8, 0.4)
) +
facet_wrap(~ type_lab,
scales = "free",
strip.position = "bottom",
labeller = label_parsed)# data_percentiles_by_subfield <-
# data_effectsizes_possible |>
# filter(type %in% unique(type)) |>
# group_by(type, subfield) |>
# summarise(
# across(
# .cols = everything(),
# .fns = list,
# .names = "{.col}_list"
# ), # just for clarity — we only care about estimate column
# .groups = "drop_last"
# ) |>
# select(type, subfield, estimate = estimate_list) |>
# unnest(estimate) |>
# group_by(type, subfield) |>
# summarise(
# percentile = c(1, 5, 10, 25, 50, 75, 90, 95, 99) / 100,
# value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
# .groups = "drop"
# ) |>
# mutate(percentile = percentile * 100) |>
# pivot_wider(names_from = type, values_from = value) |>
# select(subfield,
# percentile,
# d_native,
# d_s,
# d_z,
#
# abs_r,
# #r,
# #rho,
# #abs_rho,
# #sqrtR2,
#
# peta2,
# R2,
#
# #OR,
# abs_OR,
# #logOR,
#
# chi2,
#
# B,
# #abs_B,
# stdB,
# abs_stdB) |>
# #Wald
# arrange(subfield, percentile)
#
# data_percentiles_by_subfield |>
# mutate_if(is.numeric, round_half_up, digits = 2) |>
# kable() |>
# kable_classic(full_width = FALSE)
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_grid(subfield ~ estimator, scales = "free")
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_grid(estimator ~ subfield, scales = "free")
#
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate, fill = subfield)) +
# geom_bar(stat = "identity", width = 0.8, position = position_dodge(width = .8), color = "black") +
# theme_linedraw() +
# facet_wrap( ~ estimator, scales = "free")data_percentiles_by_subfield <-
data_effectsizes_possible |>
filter(type %in% unique(type)) |>
group_by(type, subfield) |>
summarise(
across(
.cols = everything(),
.fns = list,
.names = "{.col}_list"
), # just for clarity — we only care about estimate column
.groups = "drop_last"
) |>
select(type, subfield, estimate = estimate_list) |>
unnest(estimate) |>
group_by(type, subfield) |>
summarise(
percentile = c(1, 5, 10, 25, 50, 75, 90, 95, 99) / 100,
value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
.groups = "drop"
) |>
mutate(percentile = percentile * 100) |>
pivot_wider(names_from = subfield, values_from = value) |>
filter(type %in% c("d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"chi2",
"B",
"stdB",
"abs_stdB")) |>
mutate(type = fct_relevel(type,
"d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"chi2",
"B",
"stdB",
"abs_stdB")) |>
arrange(type, percentile)
data_percentiles_by_subfield |>
mutate_if(is.numeric, round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| type | percentile | Applied Psychology | Clinical Psychology | Developmental and Educational Psychology | Experimental and Cognitive Psychology | General Psychology | Social Psychology |
|---|---|---|---|---|---|---|---|
| d_native | 1 | 0.01 | 0.01 | 0.02 | 0.02 | 0.01 | 0.01 |
| d_native | 5 | 0.07 | 0.08 | 0.08 | 0.09 | 0.08 | 0.05 |
| d_native | 10 | 0.12 | 0.13 | 0.14 | 0.16 | 0.14 | 0.10 |
| d_native | 25 | 0.26 | 0.30 | 0.30 | 0.35 | 0.29 | 0.23 |
| d_native | 50 | 0.49 | 0.56 | 0.57 | 0.66 | 0.56 | 0.45 |
| d_native | 75 | 0.86 | 0.99 | 1.02 | 1.15 | 1.03 | 0.87 |
| d_native | 90 | 1.34 | 1.47 | 1.57 | 1.83 | 1.81 | 1.45 |
| d_native | 95 | 1.78 | 2.05 | 2.20 | 2.55 | 2.97 | 1.96 |
| d_native | 99 | 4.74 | 6.13 | 7.18 | 6.87 | 13.06 | 3.97 |
| d_s | 1 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 |
| d_s | 5 | 0.08 | 0.08 | 0.09 | 0.10 | 0.08 | 0.07 |
| d_s | 10 | 0.15 | 0.16 | 0.18 | 0.20 | 0.15 | 0.14 |
| d_s | 25 | 0.31 | 0.33 | 0.40 | 0.47 | 0.33 | 0.30 |
| d_s | 50 | 0.55 | 0.63 | 0.80 | 0.92 | 0.65 | 0.53 |
| d_s | 75 | 1.02 | 1.16 | 1.46 | 1.68 | 1.26 | 0.94 |
| d_s | 90 | 1.86 | 2.06 | 2.55 | 2.91 | 2.27 | 1.73 |
| d_s | 95 | 2.68 | 2.86 | 3.60 | 4.08 | 3.24 | 2.51 |
| d_s | 99 | 5.49 | 5.73 | 8.37 | 9.54 | 7.05 | 5.09 |
| d_z | 1 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| d_z | 5 | 0.04 | 0.04 | 0.05 | 0.05 | 0.04 | 0.04 |
| d_z | 10 | 0.08 | 0.08 | 0.09 | 0.10 | 0.07 | 0.07 |
| d_z | 25 | 0.16 | 0.17 | 0.20 | 0.24 | 0.17 | 0.15 |
| d_z | 50 | 0.28 | 0.32 | 0.41 | 0.47 | 0.33 | 0.27 |
| d_z | 75 | 0.52 | 0.59 | 0.75 | 0.86 | 0.64 | 0.48 |
| d_z | 90 | 0.94 | 1.05 | 1.31 | 1.50 | 1.16 | 0.87 |
| d_z | 95 | 1.36 | 1.46 | 1.84 | 2.10 | 1.66 | 1.27 |
| d_z | 99 | 2.83 | 2.95 | 4.36 | 4.95 | 3.60 | 2.59 |
| abs_r | 1 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| abs_r | 5 | 0.06 | 0.06 | 0.06 | 0.05 | 0.06 | 0.05 |
| abs_r | 10 | 0.11 | 0.11 | 0.11 | 0.09 | 0.11 | 0.09 |
| abs_r | 25 | 0.19 | 0.21 | 0.20 | 0.20 | 0.19 | 0.17 |
| abs_r | 50 | 0.31 | 0.33 | 0.33 | 0.35 | 0.32 | 0.30 |
| abs_r | 75 | 0.48 | 0.50 | 0.50 | 0.53 | 0.49 | 0.46 |
| abs_r | 90 | 0.66 | 0.67 | 0.67 | 0.74 | 0.66 | 0.63 |
| abs_r | 95 | 0.77 | 0.77 | 0.79 | 0.85 | 0.76 | 0.74 |
| abs_r | 99 | 0.93 | 0.92 | 0.93 | 0.97 | 0.93 | 0.92 |
| peta2 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| peta2 | 5 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 |
| peta2 | 10 | 0.01 | 0.01 | 0.01 | 0.02 | 0.01 | 0.01 |
| peta2 | 25 | 0.02 | 0.04 | 0.04 | 0.06 | 0.03 | 0.02 |
| peta2 | 50 | 0.07 | 0.11 | 0.13 | 0.16 | 0.10 | 0.07 |
| peta2 | 75 | 0.20 | 0.26 | 0.31 | 0.39 | 0.27 | 0.18 |
| peta2 | 90 | 0.43 | 0.50 | 0.57 | 0.65 | 0.53 | 0.38 |
| peta2 | 95 | 0.61 | 0.65 | 0.72 | 0.79 | 0.70 | 0.55 |
| peta2 | 99 | 0.87 | 0.89 | 0.91 | 0.93 | 0.91 | 0.81 |
| R2 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| R2 | 5 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 |
| R2 | 10 | 0.01 | 0.01 | 0.01 | 0.02 | 0.01 | 0.01 |
| R2 | 25 | 0.03 | 0.05 | 0.05 | 0.08 | 0.04 | 0.04 |
| R2 | 50 | 0.11 | 0.13 | 0.12 | 0.20 | 0.12 | 0.12 |
| R2 | 75 | 0.25 | 0.30 | 0.27 | 0.39 | 0.26 | 0.27 |
| R2 | 90 | 0.48 | 0.51 | 0.46 | 0.62 | 0.45 | 0.43 |
| R2 | 95 | 0.64 | 0.64 | 0.59 | 0.75 | 0.59 | 0.52 |
| R2 | 99 | 0.95 | 0.80 | 0.86 | 0.91 | 0.91 | 0.73 |
| abs_OR | 1 | 1.00 | 1.00 | 1.01 | 1.01 | 1.01 | 1.00 |
| abs_OR | 5 | 1.03 | 1.03 | 1.04 | 1.03 | 1.04 | 1.03 |
| abs_OR | 10 | 1.06 | 1.06 | 1.10 | 1.07 | 1.08 | 1.08 |
| abs_OR | 25 | 1.25 | 1.23 | 1.30 | 1.23 | 1.26 | 1.30 |
| abs_OR | 50 | 1.71 | 1.71 | 1.82 | 1.79 | 1.72 | 1.79 |
| abs_OR | 75 | 2.61 | 2.71 | 2.91 | 3.36 | 2.70 | 2.80 |
| abs_OR | 90 | 4.64 | 4.98 | 5.56 | 7.27 | 5.00 | 5.18 |
| abs_OR | 95 | 7.08 | 7.94 | 9.43 | 14.79 | 8.86 | 8.40 |
| abs_OR | 99 | 23.66 | 24.75 | 39.32 | 72.74 | 33.33 | 23.47 |
| chi2 | 1 | 0.06 | 0.03 | 0.03 | 0.01 | 0.04 | 0.05 |
| chi2 | 5 | 1.00 | 0.51 | 0.55 | 0.24 | 0.63 | 1.00 |
| chi2 | 10 | 2.28 | 1.42 | 1.59 | 0.80 | 1.88 | 2.24 |
| chi2 | 25 | 6.40 | 4.90 | 5.32 | 3.79 | 6.07 | 6.03 |
| chi2 | 50 | 23.40 | 13.33 | 14.99 | 10.67 | 19.20 | 18.18 |
| chi2 | 75 | 175.85 | 62.48 | 64.27 | 37.84 | 118.93 | 90.40 |
| chi2 | 90 | 761.99 | 348.78 | 321.33 | 168.85 | 636.18 | 450.36 |
| chi2 | 95 | 1571.09 | 866.00 | 768.67 | 449.24 | 1387.13 | 946.78 |
| chi2 | 99 | 5286.28 | 4386.75 | 3533.51 | 2569.91 | 5867.32 | 3723.77 |
| B | 1 | -0.62 | -4.82 | -0.82 | -3.82 | -1.97 | -0.42 |
| B | 5 | 0.00 | -0.09 | 0.00 | -0.04 | -0.13 | 0.00 |
| B | 10 | 0.02 | 0.01 | 0.02 | 0.01 | 0.01 | 0.02 |
| B | 25 | 0.10 | 0.06 | 0.09 | 0.08 | 0.08 | 0.09 |
| B | 50 | 0.29 | 0.28 | 0.28 | 0.35 | 0.24 | 0.26 |
| B | 75 | 0.74 | 0.99 | 0.97 | 1.25 | 0.60 | 0.74 |
| B | 90 | 2.11 | 3.37 | 2.88 | 4.16 | 1.81 | 2.15 |
| B | 95 | 4.92 | 7.48 | 6.18 | 9.89 | 4.30 | 4.48 |
| B | 99 | 39.71 | 30.81 | 28.42 | 73.16 | 38.14 | 19.65 |
| stdB | 1 | -0.21 | -0.53 | -0.37 | -1.98 | -0.44 | -0.28 |
| stdB | 5 | 0.01 | 0.00 | 0.00 | 0.00 | -0.09 | 0.01 |
| stdB | 10 | 0.04 | 0.02 | 0.03 | 0.02 | 0.02 | 0.03 |
| stdB | 25 | 0.12 | 0.09 | 0.10 | 0.11 | 0.11 | 0.10 |
| stdB | 50 | 0.24 | 0.22 | 0.21 | 0.26 | 0.22 | 0.20 |
| stdB | 75 | 0.45 | 0.42 | 0.39 | 0.58 | 0.39 | 0.38 |
| stdB | 90 | 0.81 | 1.00 | 0.79 | 1.89 | 0.62 | 0.71 |
| stdB | 95 | 1.77 | 2.34 | 1.74 | 4.63 | 0.87 | 1.42 |
| stdB | 99 | 15.04 | 13.68 | 12.11 | 47.85 | 6.40 | 9.05 |
| abs_stdB | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| abs_stdB | 5 | 0.02 | 0.02 | 0.02 | 0.02 | 0.03 | 0.02 |
| abs_stdB | 10 | 0.05 | 0.04 | 0.05 | 0.04 | 0.06 | 0.05 |
| abs_stdB | 25 | 0.13 | 0.11 | 0.11 | 0.13 | 0.12 | 0.11 |
| abs_stdB | 50 | 0.24 | 0.23 | 0.22 | 0.28 | 0.23 | 0.21 |
| abs_stdB | 75 | 0.45 | 0.44 | 0.40 | 0.63 | 0.40 | 0.39 |
| abs_stdB | 90 | 0.83 | 1.08 | 0.83 | 2.11 | 0.64 | 0.73 |
| abs_stdB | 95 | 1.80 | 2.59 | 1.91 | 5.80 | 0.90 | 1.48 |
| abs_stdB | 99 | 15.81 | 15.13 | 14.36 | 49.40 | 7.16 | 9.44 |
## # A tibble: 18 Ă— 2
## type n
## <chr> <int>
## 1 B 104679
## 2 OR 43885
## 3 R2 7693
## 4 Wald 5941
## 5 abs_OR 43868
## 6 abs_r 130650
## 7 abs_rho 3135
## 8 abs_stdB 143581
## 9 chi2 185020
## 10 d_native 23089
## 11 d_s 454687
## 12 d_z 454687
## 13 logOR 43885
## 14 peta2 1219293
## 15 r 130650
## 16 rho 3135
## 17 sqrtR2 8150
## 18 stdB 143581
data_effectsizes_possible |>
filter(type == "d_native") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d native",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")data_effectsizes_possible |>
filter(type == "d_s") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d_s",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")data_effectsizes_possible |>
filter(type == "d_z") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d_s",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")IS THIS TRIMMING THINGS WEIRDLY? WHERE IS THE ABSOLUTE SCORING IN THE BELOW?
plot_es <- function(data, x, trim_lower = 0, trim_upper = 1, binwidth = 0.1, xlab, title, subtitle) {
data_subset <- data |>
filter(type == x)
# plot limits
p1 <- quantile(data_subset$estimate, trim_lower, na.rm = TRUE)
p99 <- quantile(data_subset$estimate, trim_upper, na.rm = TRUE)
ggplot(data_subset, aes(x = estimate)) +
geom_histogram(binwidth = binwidth, boundary = 0) +
theme_linedraw() +
scale_x_continuous(limits = c(p1, p99), breaks = scales::breaks_pretty(n = 10)) +
scale_y_continuous(breaks = scales::breaks_pretty(n = 6)) +
labs(title = title,
subtitle = subtitle,
x = xlab,
y = "Count")
}
p_d_native <-
plot_es(data_effectsizes_possible, "d_native", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)),
title = expression("Absolute Cohen's "*italic(d)),
subtitle = "0–99th percentile range")
p_d_s <-
plot_es(data_effectsizes_possible, "d_s", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)[s]),
title = expression("Absolute Cohen's "*italic(d)[s]*" estimated from "*italic(t)*"-test"),
subtitle = "0–99th percentile range")
p_d_z <-
plot_es(data_effectsizes_possible, "d_z", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)[z]),
title = expression("Absolute Cohen's "*italic(d)[z]*" estimated from "*italic(t)*"-test"),
subtitle = "0–99th percentile range")
p_peta2 <-
plot_es(data_effectsizes_possible, "peta2", trim_lower = 0.01, trim_upper = 0.99, binwidth = 0.01,
xlab = expression(italic(eta)[p]^2),
title = expression(italic(eta)[p]^2*" estimated from "*italic(F)*"-test"),
subtitle = "1–99th percentile range")
p_abs_r <-
plot_es(data_effectsizes_possible, "abs_r", binwidth = 0.1,
xlab = expression("Pearson's "*italic(r)),
title = expression("Absolute Pearson's "*italic(r)),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
# data_effectsizes_possible |>
# mutate(r_out_of_bounds = case_when(r < -1 ~ TRUE,
# r > 1 ~ TRUE,
# TRUE ~ FALSE)) |>
# summarize(percent_r_out_of_bounds = mean(r_out_of_bounds)*100)
p_abs_rho <-
plot_es(data_effectsizes_possible, "rho", binwidth = 0.1,
xlab = expression(italic(rho)),
title = expression("Absolute "*italic(rho)),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
p_sqrtR2 <-
plot_es(data_effectsizes_possible, "sqrtR2", binwidth = 0.1,
xlab = expression("sqrt R"^2),
title = expression("sqrt R"^2),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
p_R2 <-
plot_es(data_effectsizes_possible, "R2", binwidth = 0.1,
xlab = expression("R"^2),
title = expression("R"^2),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
# dat_es |>
# mutate(R2_out_of_bounds = case_when(R2 < -1 ~ TRUE,
# R2 > 1 ~ TRUE,
# TRUE ~ FALSE)) |>
# summarize(percent_R2_out_of_bounds = mean(R2_out_of_bounds)*100)
p_stdB <-
plot_es(data_effectsizes_possible, "stdB", trim_lower = 0.01, trim_upper = 0.95, binwidth = 0.01,
xlab = expression(beta),
title = expression(beta),
subtitle = "1–95th percentile range")
p_B <-
plot_es(data_effectsizes_possible, "B", trim_lower = 0.01, trim_upper = 0.95, binwidth = 0.1,
xlab = expression("B"),
title = expression("B"),
subtitle = "1–95th percentile range")
p_wald <-
plot_es(data_effectsizes_possible, "Wald", trim_lower = 0, trim_upper = 0.95, binwidth = 1,
xlab = expression("Wald"),
title = expression("Wald"),
subtitle = "1–95th percentile range")
p_chi2 <-
plot_es(data_effectsizes_possible, "chi2", trim_lower = 0, trim_upper = 0.9, binwidth = 5,
xlab = expression(chi^2),
title = expression(chi^2),
subtitle = "0–90th percentile range of positive values") +
coord_cartesian(xlim = c(0, NA))
p_OR <-
plot_es(data_effectsizes_possible, "OR", trim_lower = 0.01, trim_upper = 0.98,
xlab = expression("OR"),
title = expression("OR"),
subtitle = "1–98th percentile range")
p_abs_OR <-
plot_es(data_effectsizes_possible, "abs_OR", trim_upper = 0.98,
xlab = expression("Absolute OR"),
title = expression("Absolute OR"),
subtitle = "0–98th percentile range")
p_logOR <-
plot_es(data_effectsizes_possible, "logOR", trim_lower = 0.01, trim_upper = 0.99,
xlab = expression("log-odds"),
title = expression("log-odds"),
subtitle = "1–99th percentile range")
# plot_es(data_effectsizes_possible, z, trim_lower = 0.01, trim_upper = 0.98,
# xlab = expression("z-score"),
# title = expression("z-score"),
# subtitle = "1–98th percentile range") +
# geom_vline(xintercept = 1.96, color = "pink", linetype = "dashed")
#
# plot_es(dat_p, p_val, trim_lower = 0, trim_upper = 1, binwidth = 0.001,
# xlab = expression(italic(p)*" value"),
# title = expression(italic(p)*" value"),
# subtitle = "Between 0 and .1") +
# coord_cartesian(xlim = c(0, 0.1))
#
# plot_es(dat_p, p_implied, trim_lower = 0, trim_upper = 1, binwidth = 0.001,
# xlab = expression("Implied "*italic(p)*" value"),
# title = expression("Implied "*italic(p)*" value"),
# subtitle = "Between 0 and .1") +
# coord_cartesian(xlim = c(0, 0.1))quantile_regression_and_plot <- function(data, es_type, label){
dat_for_reg <- data |>
filter(type == es_type) |>
mutate(estimate = round_half_up(estimate, 2)) |>
count(subfield, estimate)
# fit quantile regressions at multiple quantiles
#fit_05 <- rq(estimate ~ 0 + subfield, tau = 0.05, weights = n, method = "fn", data = dat_for_reg)
fit_10 <- rq(estimate ~ 0 + subfield, tau = 0.10, weights = n, method = "fn", data = dat_for_reg)
fit_25 <- rq(estimate ~ 0 + subfield, tau = 0.25, weights = n, method = "fn", data = dat_for_reg)
fit_50 <- rq(estimate ~ 0 + subfield, tau = 0.50, weights = n, method = "fn", data = dat_for_reg)
fit_75 <- rq(estimate ~ 0 + subfield, tau = 0.75, weights = n, method = "fn", data = dat_for_reg)
fit_90 <- rq(estimate ~ 0 + subfield, tau = 0.90, weights = n, method = "fn", data = dat_for_reg)
fit_95 <- rq(estimate ~ 0 + subfield, tau = 0.95, weights = n, method = "fn", data = dat_for_reg)
fit_99 <- rq(estimate ~ 0 + subfield, tau = 0.99, weights = n, method = "fn", data = dat_for_reg)
# wrangle and plot
res <- bind_rows(
# summary(fit_05, se = "nid")$coefficients |>
# as.data.frame() |>
# rownames_to_column(var = "subfield") |>
# mutate(percentile = 5),
summary(fit_10, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 10),
summary(fit_25, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 25),
summary(fit_50, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 50),
summary(fit_75, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 75),
summary(fit_90, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 90),
summary(fit_95, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 95),
summary(fit_99, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 99)
) |>
mutate(subfield = str_remove(subfield, "subfield"),
percentile = as.factor(percentile)) |>
rename(estimate = Value,
se = `Std. Error`) |>
mutate(subfield = fct_relevel(subfield,
"Social Psychology",
"Applied Psychology",
"Clinical Psychology",
"Developmental and Educational Psychology",
"Experimental and Cognitive Psychology",
"General Psychology"))
# ggplot(res, aes(estimate, subfield, color = percentile)) +
# geom_linerangeh(aes(xmin = estimate - se*1.96, xmax = estimate + se*1.96),
# position = position_dodge(width = 0.75)) +
# geom_point(position = position_dodge(width = 0.75)) +
# theme_linedraw() +
# ylab("") +
# scale_x_continuous(name = label, breaks = scales::breaks_pretty(n = 8)) +
# guides(color = guide_legend(reverse = TRUE))
plot <- res |>
filter(percentile %in% c(10, 25, 50, 75, 90, 95)) |>
ggplot(aes(estimate, percentile, color = subfield)) +
geom_linerangeh(aes(xmin = estimate - se*1.96, xmax = estimate + se*1.96),
position = position_dodge(width = 0.75)) +
geom_point(position = position_dodge(width = 0.75)) +
theme_linedraw() +
scale_x_continuous(name = label, breaks = scales::breaks_pretty(n = 8)) +
ylab("Percentile") +
guides(color = guide_legend(reverse = TRUE))
return(list(res = res,
plot = plot))
}
quantile_regression_and_plot(data_effectsizes_possible, "d_native", "Cohen's d")## $res
## subfield estimate se t value
## 1 Applied Psychology 0.12 0.02025505 5.9244477
## 2 Clinical Psychology 0.13 0.03296069 3.9440926
## 3 Developmental and Educational Psychology 0.14 0.02745149 5.0999045
## 4 Experimental and Cognitive Psychology 0.16 0.03445154 4.6442044
## 5 General Psychology 0.14 0.02730476 5.1273117
## 6 Social Psychology 0.10 0.02036129 4.9112800
## 7 Applied Psychology 0.26 0.03382380 7.6868952
## 8 Clinical Psychology 0.30 0.03494658 8.5845307
## 9 Developmental and Educational Psychology 0.30 0.03735200 8.0316974
## 10 Experimental and Cognitive Psychology 0.35 0.04154976 8.4236348
## 11 General Psychology 0.29 0.03715235 7.8056982
## 12 Social Psychology 0.23 0.03022329 7.6100243
## 13 Applied Psychology 0.49 0.04207754 11.6451674
## 14 Clinical Psychology 0.56 0.05589554 10.0186892
## 15 Developmental and Educational Psychology 0.57 0.05159615 11.0473369
## 16 Experimental and Cognitive Psychology 0.66 0.05623287 11.7369071
## 17 General Psychology 0.56 0.04861928 11.5180654
## 18 Social Psychology 0.45 0.04229824 10.6387412
## 19 Applied Psychology 0.86 0.08268040 10.4014979
## 20 Clinical Psychology 0.99 0.08736645 11.3315804
## 21 Developmental and Educational Psychology 1.02 0.08149528 12.5160618
## 22 Experimental and Cognitive Psychology 1.15 0.08629565 13.3262793
## 23 General Psychology 1.03 0.10470207 9.8374371
## 24 Social Psychology 0.87 0.10955944 7.9408948
## 25 Applied Psychology 1.34 0.14684914 9.1250111
## 26 Clinical Psychology 1.47 0.16009477 9.1820616
## 27 Developmental and Educational Psychology 1.57 0.18300996 8.5787679
## 28 Experimental and Cognitive Psychology 1.83 0.22824147 8.0178244
## 29 General Psychology 1.81 0.25939520 6.9777699
## 30 Social Psychology 1.45 0.18325162 7.9126175
## 31 Applied Psychology 1.78 0.32984245 5.3965157
## 32 Clinical Psychology 2.05 0.37920809 5.4060028
## 33 Developmental and Educational Psychology 2.20 0.44974185 4.8916951
## 34 Experimental and Cognitive Psychology 2.56 0.42841871 5.9754627
## 35 General Psychology 2.97 0.91623412 3.2415296
## 36 Social Psychology 1.97 0.34362966 5.7329160
## 37 Applied Psychology 4.75 9.13642929 0.5198968
## 38 Clinical Psychology 6.28 6.50262859 0.9657633
## 39 Developmental and Educational Psychology 7.30 7.02872614 1.0385950
## 40 Experimental and Cognitive Psychology 6.93 6.46797322 1.0714330
## 41 General Psychology 13.06 22.78075401 0.5732909
## 42 Social Psychology 4.01 2.26281086 1.7721322
## Pr(>|t|) percentile
## 1 0.000000003630188594173 10
## 2 0.000082623657119418326 10
## 3 0.000000369052054915997 10
## 4 0.000003616414766760556 10
## 5 0.000000319735750542449 10
## 6 0.000000971794473336729 10
## 7 0.000000000000022648550 25
## 8 0.000000000000000000000 25
## 9 0.000000000000001554312 25
## 10 0.000000000000000000000 25
## 11 0.000000000000009103829 25
## 12 0.000000000000040412118 25
## 13 0.000000000000000000000 50
## 14 0.000000000000000000000 50
## 15 0.000000000000000000000 50
## 16 0.000000000000000000000 50
## 17 0.000000000000000000000 50
## 18 0.000000000000000000000 50
## 19 0.000000000000000000000 75
## 20 0.000000000000000000000 75
## 21 0.000000000000000000000 75
## 22 0.000000000000000000000 75
## 23 0.000000000000000000000 75
## 24 0.000000000000003108624 75
## 25 0.000000000000000000000 90
## 26 0.000000000000000000000 90
## 27 0.000000000000000000000 90
## 28 0.000000000000001776357 90
## 29 0.000000000003959499395 90
## 30 0.000000000000003996803 90
## 31 0.000000075287193368467 95
## 32 0.000000071458190920026 95
## 33 0.000001072524674849973 95
## 34 0.000000002671314724978 95
## 35 0.001206757165709504420 95
## 36 0.000000011237439867529 95
## 37 0.603188125879168701715 99
## 38 0.334269370021937461956 99
## 39 0.299107878668044113724 99
## 40 0.284092945128494367424 99
## 41 0.566506629588456878110 99
## 42 0.076511752624263795752 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.15 0.02714137 5.526618
## 2 Clinical Psychology 0.16 0.03155475 5.070552
## 3 Developmental and Educational Psychology 0.18 0.03351159 5.371276
## 4 Experimental and Cognitive Psychology 0.20 0.03642663 5.490489
## 5 General Psychology 0.15 0.03046962 4.922936
## 6 Social Psychology 0.14 0.02791879 5.014544
## 7 Applied Psychology 0.31 0.03021548 10.259641
## 8 Clinical Psychology 0.33 0.03278682 10.065021
## 9 Developmental and Educational Psychology 0.40 0.04145246 9.649609
## 10 Experimental and Cognitive Psychology 0.47 0.04634562 10.141196
## 11 General Psychology 0.33 0.03618208 9.120536
## 12 Social Psychology 0.30 0.03626111 8.273326
## 13 Applied Psychology 0.55 0.04832835 11.380484
## 14 Clinical Psychology 0.63 0.05244109 12.013481
## 15 Developmental and Educational Psychology 0.80 0.06298633 12.701168
## 16 Experimental and Cognitive Psychology 0.92 0.06486183 14.183998
## 17 General Psychology 0.65 0.05787167 11.231748
## 18 Social Psychology 0.53 0.04142719 12.793530
## 19 Applied Psychology 1.02 0.11079011 9.206598
## 20 Clinical Psychology 1.16 0.11709577 9.906421
## 21 Developmental and Educational Psychology 1.46 0.12021213 12.145197
## 22 Experimental and Cognitive Psychology 1.68 0.13131259 12.793899
## 23 General Psychology 1.26 0.11759177 10.715035
## 24 Social Psychology 0.94 0.10360317 9.073081
## 25 Applied Psychology 1.86 0.26462838 7.028725
## 26 Clinical Psychology 2.06 0.23350514 8.822075
## 27 Developmental and Educational Psychology 2.55 0.27367800 9.317519
## 28 Experimental and Cognitive Psychology 2.91 0.28100544 10.355672
## 29 General Psychology 2.28 0.28641447 7.960492
## 30 Social Psychology 1.73 0.25824879 6.698967
## 31 Applied Psychology 2.68 0.50627074 5.293610
## 32 Clinical Psychology 2.86 0.44845249 6.377487
## 33 Developmental and Educational Psychology 3.60 0.50272196 7.161016
## 34 Experimental and Cognitive Psychology 4.08 0.54850606 7.438386
## 35 General Psychology 3.24 0.49076828 6.601894
## 36 Social Psychology 2.51 0.44637595 5.623063
## 37 Applied Psychology 5.49 2.45074491 2.240135
## 38 Clinical Psychology 5.73 2.46505521 2.324492
## 39 Developmental and Educational Psychology 8.38 3.57819406 2.341964
## 40 Experimental and Cognitive Psychology 9.55 3.61446409 2.642162
## 41 General Psychology 7.05 2.67906596 2.631514
## 42 Social Psychology 5.09 1.82511641 2.788863
## Pr(>|t|) percentile
## 1 0.000000033773718044472 10
## 2 0.000000406436506894536 10
## 3 0.000000080595131013439 10
## 4 0.000000041431314601326 10
## 5 0.000000871277638925250 10
## 6 0.000000544135407398727 10
## 7 0.000000000000000000000 25
## 8 0.000000000000000000000 25
## 9 0.000000000000000000000 25
## 10 0.000000000000000000000 25
## 11 0.000000000000000000000 25
## 12 0.000000000000000000000 25
## 13 0.000000000000000000000 50
## 14 0.000000000000000000000 50
## 15 0.000000000000000000000 50
## 16 0.000000000000000000000 50
## 17 0.000000000000000000000 50
## 18 0.000000000000000000000 50
## 19 0.000000000000000000000 75
## 20 0.000000000000000000000 75
## 21 0.000000000000000000000 75
## 22 0.000000000000000000000 75
## 23 0.000000000000000000000 75
## 24 0.000000000000000000000 75
## 25 0.000000000002272848576 90
## 26 0.000000000000000000000 90
## 27 0.000000000000000000000 90
## 28 0.000000000000000000000 90
## 29 0.000000000000001998401 90
## 30 0.000000000022552182344 90
## 31 0.000000123421508346766 95
## 32 0.000000000191005211647 95
## 33 0.000000000000879074591 95
## 34 0.000000000000113686838 95
## 35 0.000000000043442138775 95
## 36 0.000000019453775923495 95
## 37 0.025112143860383850935 99
## 38 0.020126484801269217684 99
## 39 0.019209352361759357564 99
## 40 0.008255452747476965669 99
## 41 0.008518424287752734969 99
## 42 0.005303053180681915890 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.08 0.02479629 3.226289
## 2 Clinical Psychology 0.08 0.02301171 3.476491
## 3 Developmental and Educational Psychology 0.09 0.02711581 3.319096
## 4 Experimental and Cognitive Psychology 0.10 0.02522774 3.963891
## 5 General Psychology 0.07 0.02223441 3.148273
## 6 Social Psychology 0.07 0.01702150 4.112446
## 7 Applied Psychology 0.16 0.01840320 8.694142
## 8 Clinical Psychology 0.17 0.02277163 7.465429
## 9 Developmental and Educational Psychology 0.20 0.03018703 6.625362
## 10 Experimental and Cognitive Psychology 0.24 0.03276596 7.324676
## 11 General Psychology 0.17 0.02750305 6.181133
## 12 Social Psychology 0.15 0.02526587 5.936862
## 13 Applied Psychology 0.28 0.03434096 8.153528
## 14 Clinical Psychology 0.32 0.03642223 8.785843
## 15 Developmental and Educational Psychology 0.41 0.04425927 9.263596
## 16 Experimental and Cognitive Psychology 0.47 0.04866431 9.658002
## 17 General Psychology 0.33 0.03959094 8.335241
## 18 Social Psychology 0.27 0.03030875 8.908320
## 19 Applied Psychology 0.52 0.07974718 6.520607
## 20 Clinical Psychology 0.59 0.07970071 7.402695
## 21 Developmental and Educational Psychology 0.75 0.08552992 8.768861
## 22 Experimental and Cognitive Psychology 0.86 0.09361702 9.186364
## 23 General Psychology 0.64 0.08800976 7.271921
## 24 Social Psychology 0.48 0.07579761 6.332653
## 25 Applied Psychology 0.94 0.19010492 4.944638
## 26 Clinical Psychology 1.05 0.16875255 6.222128
## 27 Developmental and Educational Psychology 1.31 0.20336861 6.441506
## 28 Experimental and Cognitive Psychology 1.50 0.20812884 7.207074
## 29 General Psychology 1.16 0.20010970 5.796820
## 30 Social Psychology 0.87 0.18723649 4.646530
## 31 Applied Psychology 1.37 0.36219104 3.782534
## 32 Clinical Psychology 1.46 0.32703981 4.464288
## 33 Developmental and Educational Psychology 1.84 0.36930990 4.982266
## 34 Experimental and Cognitive Psychology 2.10 0.41081990 5.111729
## 35 General Psychology 1.66 0.35988082 4.612638
## 36 Social Psychology 1.27 0.33262295 3.818137
## 37 Applied Psychology 2.84 1.81010735 1.568968
## 38 Clinical Psychology 2.95 1.78012304 1.657189
## 39 Developmental and Educational Psychology 4.36 2.59246281 1.681798
## 40 Experimental and Cognitive Psychology 4.95 2.82424846 1.752679
## 41 General Psychology 3.60 2.02280896 1.779703
## 42 Social Psychology 2.59 1.43264653 1.807843
## Pr(>|t|) percentile
## 1 0.0012625828731929189530 10
## 2 0.0005125755661539166397 10
## 3 0.0009099039976663281237 10
## 4 0.0000748222195823267100 10
## 5 0.0016525690771183043637 10
## 6 0.0000398112125403748962 10
## 7 0.0000000000000000000000 25
## 8 0.0000000000000981437154 25
## 9 0.0000000000384923204422 25
## 10 0.0000000000002797762022 25
## 11 0.0000000006897606930067 25
## 12 0.0000000031121683008450 25
## 13 0.0000000000000004440892 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000773183739256 75
## 20 0.0000000000001569855357 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000000000000 75
## 23 0.0000000000004125588760 75
## 24 0.0000000002632591922236 75
## 25 0.0000007891725244402181 90
## 26 0.0000000005326672436468 90
## 27 0.0000000001300173302354 90
## 28 0.0000000000006619149673 90
## 29 0.0000000071957093616959 90
## 30 0.0000034670490496324646 90
## 31 0.0001571508889752770699 95
## 32 0.0000082198464756988443 95
## 33 0.0000006507746248551882 95
## 34 0.0000003317688088699811 95
## 35 0.0000040805414616151836 95
## 36 0.0001361783425619655929 95
## 37 0.1167221395142845619119 99
## 38 0.0975474155798852216037 99
## 39 0.0926737277113329760425 99
## 40 0.0797217603012183584354 99
## 41 0.0751884968393823349686 99
## 42 0.0706942333431048730574 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.11 0.02641905 4.163662
## 2 Clinical Psychology 0.11 0.03033243 3.626482
## 3 Developmental and Educational Psychology 0.11 0.02575230 4.271463
## 4 Experimental and Cognitive Psychology 0.09 0.02869779 3.136130
## 5 General Psychology 0.11 0.02606077 4.220904
## 6 Social Psychology 0.09 0.02660593 3.382705
## 7 Applied Psychology 0.19 0.02287549 8.305832
## 8 Clinical Psychology 0.21 0.02894397 7.255396
## 9 Developmental and Educational Psychology 0.20 0.02866908 6.976156
## 10 Experimental and Cognitive Psychology 0.20 0.03042684 6.573143
## 11 General Psychology 0.19 0.02578887 7.367518
## 12 Social Psychology 0.17 0.02632835 6.456918
## 13 Applied Psychology 0.31 0.03397490 9.124382
## 14 Clinical Psychology 0.33 0.03086308 10.692386
## 15 Developmental and Educational Psychology 0.33 0.03311746 9.964532
## 16 Experimental and Cognitive Psychology 0.35 0.03406649 10.274026
## 17 General Psychology 0.32 0.03351415 9.548206
## 18 Social Psychology 0.30 0.03421523 8.768026
## 19 Applied Psychology 0.48 0.04901892 9.792138
## 20 Clinical Psychology 0.50 0.04502396 11.105198
## 21 Developmental and Educational Psychology 0.50 0.04459635 11.211680
## 22 Experimental and Cognitive Psychology 0.53 0.05172564 10.246370
## 23 General Psychology 0.49 0.04513053 10.857395
## 24 Social Psychology 0.46 0.04607461 9.983806
## 25 Applied Psychology 0.66 0.06164445 10.706560
## 26 Clinical Psychology 0.67 0.06066486 11.044285
## 27 Developmental and Educational Psychology 0.67 0.06438075 10.406837
## 28 Experimental and Cognitive Psychology 0.74 0.06559495 11.281356
## 29 General Psychology 0.66 0.06080845 10.853754
## 30 Social Psychology 0.63 0.06208050 10.148114
## 31 Applied Psychology 0.77 0.07300699 10.546935
## 32 Clinical Psychology 0.77 0.06671492 11.541646
## 33 Developmental and Educational Psychology 0.79 0.07624766 10.360974
## 34 Experimental and Cognitive Psychology 0.85 0.06311960 13.466498
## 35 General Psychology 0.76 0.07201690 10.553079
## 36 Social Psychology 0.74 0.08402676 8.806718
## 37 Applied Psychology 0.93 0.07915761 11.748713
## 38 Clinical Psychology 0.92 0.07789972 11.810056
## 39 Developmental and Educational Psychology 0.93 0.06313080 14.731319
## 40 Experimental and Cognitive Psychology 0.97 0.04020089 24.128817
## 41 General Psychology 0.93 0.07808410 11.910235
## 42 Social Psychology 0.92 0.08696459 10.579019
## Pr(>|t|) percentile
## 1 0.0000359104096989693033 10
## 2 0.0003117420167653150997 10
## 3 0.0000225768237307466535 10
## 4 0.0017957120880454091605 10
## 5 0.0000281022088639559797 10
## 6 0.0007644916583455785286 10
## 7 0.0000000000000006661338 25
## 8 0.0000000000012434497876 25
## 9 0.0000000000080442319472 25
## 10 0.0000000001071256416907 25
## 11 0.0000000000005777600620 25
## 12 0.0000000002207625193762 25
## 13 0.0000000000000000000000 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000000000000000 75
## 20 0.0000000000000000000000 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000000000000 75
## 23 0.0000000000000000000000 75
## 24 0.0000000000000000000000 75
## 25 0.0000000000000000000000 90
## 26 0.0000000000000000000000 90
## 27 0.0000000000000000000000 90
## 28 0.0000000000000000000000 90
## 29 0.0000000000000000000000 90
## 30 0.0000000000000000000000 90
## 31 0.0000000000000000000000 95
## 32 0.0000000000000000000000 95
## 33 0.0000000000000000000000 95
## 34 0.0000000000000000000000 95
## 35 0.0000000000000000000000 95
## 36 0.0000000000000000000000 95
## 37 0.0000000000000000000000 99
## 38 0.0000000000000000000000 99
## 39 0.0000000000000000000000 99
## 40 0.0000000000000000000000 99
## 41 0.0000000000000000000000 99
## 42 0.0000000000000000000000 99
##
## $plot
quantile_regression_and_plot(data_effectsizes_possible, "peta2", "peta2") # throws error, needs fixing ## $res
## subfield estimate se t value
## 1 Applied Psychology 0.01 0.007497950 1.3336979
## 2 Clinical Psychology 0.01 0.012560465 0.7961488
## 3 Developmental and Educational Psychology 0.01 0.005848786 1.7097565
## 4 Experimental and Cognitive Psychology 0.02 0.010509878 1.9029716
## 5 General Psychology 0.01 0.006550580 1.5265824
## 6 Social Psychology 0.01 0.007760665 1.2885494
## 7 Applied Psychology 0.02 0.016694379 1.1980080
## 8 Clinical Psychology 0.04 0.018644134 2.1454469
## 9 Developmental and Educational Psychology 0.04 0.017363298 2.3037098
## 10 Experimental and Cognitive Psychology 0.06 0.019500430 3.0768553
## 11 General Psychology 0.03 0.014585035 2.0569028
## 12 Social Psychology 0.02 0.011519548 1.7361793
## 13 Applied Psychology 0.07 0.026701932 2.6215331
## 14 Clinical Psychology 0.11 0.033548040 3.2788800
## 15 Developmental and Educational Psychology 0.13 0.038186279 3.4043642
## 16 Experimental and Cognitive Psychology 0.16 0.040547114 3.9460268
## 17 General Psychology 0.10 0.038880214 2.5720023
## 18 Social Psychology 0.07 0.027637522 2.5327886
## 19 Applied Psychology 0.20 0.083471895 2.3960161
## 20 Clinical Psychology 0.26 0.074576536 3.4863513
## 21 Developmental and Educational Psychology 0.31 0.082475666 3.7586844
## 22 Experimental and Cognitive Psychology 0.39 0.085801890 4.5453544
## 23 General Psychology 0.27 0.087510211 3.0853542
## 24 Social Psychology 0.18 0.069117286 2.6042689
## 25 Applied Psychology 0.43 0.172452844 2.4934352
## 26 Clinical Psychology 0.50 0.125604654 3.9807442
## 27 Developmental and Educational Psychology 0.57 0.122824509 4.6407676
## 28 Experimental and Cognitive Psychology 0.65 0.105098782 6.1846578
## 29 General Psychology 0.53 0.150663334 3.5177769
## 30 Social Psychology 0.38 0.162973967 2.3316607
## 31 Applied Psychology 0.61 0.213120012 2.8622371
## 32 Clinical Psychology 0.65 0.156194413 4.1614805
## 33 Developmental and Educational Psychology 0.72 0.131610279 5.4706973
## 34 Experimental and Cognitive Psychology 0.79 0.099576792 7.9335755
## 35 General Psychology 0.70 0.162918160 4.2966358
## 36 Social Psychology 0.55 0.211396232 2.6017493
## 37 Applied Psychology 0.87 0.245079251 3.5498721
## 38 Clinical Psychology 0.89 0.174485234 5.1007182
## 39 Developmental and Educational Psychology 0.91 0.124263367 7.3231558
## 40 Experimental and Cognitive Psychology 0.93 0.077293721 12.0320252
## 41 General Psychology 0.91 0.139173681 6.5385926
## 42 Social Psychology 0.81 0.304399690 2.6609751
## Pr(>|t|) percentile
## 1 0.1828089322840733555 10
## 2 0.4262604075108600288 10
## 3 0.0878279398725378968 10
## 4 0.0575226138546194310 10
## 5 0.1273918248017316124 10
## 6 0.1980514334455880654 10
## 7 0.2313869102114560761 25
## 8 0.0323177932497431541 25
## 9 0.0215798352834997154 25
## 10 0.0021873064075570436 25
## 11 0.0401273351472526407 25
## 12 0.0830454571720915524 25
## 13 0.0089755340326722610 50
## 14 0.0011024925585678691 50
## 15 0.0007074255792196560 50
## 16 0.0000888621628361008 50
## 17 0.0103507091870844725 50
## 18 0.0115696259350213104 50
## 19 0.0168796279877485578 75
## 20 0.0005254344775638131 75
## 21 0.0001875042996382081 75
## 22 0.0000066351957048738 75
## 23 0.0021267110194758132 75
## 24 0.0094350832759300118 75
## 25 0.0129196375776954309 90
## 26 0.0000771173345492926 90
## 27 0.0000042655972998240 90
## 28 0.0000000011516780862 90
## 29 0.0004680881981227181 90
## 30 0.0200486442094831574 90
## 31 0.0043533820028096581 95
## 32 0.0000362455001583495 95
## 33 0.0000000658953569488 95
## 34 0.0000000000000104361 95
## 35 0.0000202285214205844 95
## 36 0.0095038831758742504 95
## 37 0.0004156013608507259 99
## 38 0.0000004544932576955 99
## 39 0.0000000000007833734 99
## 40 0.0000000000000000000 99
## 41 0.0000000001329625299 99
## 42 0.0079999960594459019 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.0100000 0.006312746 1.584097
## 2 Clinical Psychology 0.0100000 0.005802359 1.723437
## 3 Developmental and Educational Psychology 0.0100000 0.005772551 1.732336
## 4 Experimental and Cognitive Psychology 0.0200000 0.014404906 1.388416
## 5 General Psychology 0.0100000 0.005976178 1.673310
## 6 Social Psychology 0.0100000 0.005959635 1.677955
## 7 Applied Psychology 0.0300000 0.014055493 2.134397
## 8 Clinical Psychology 0.0500000 0.021531841 2.322142
## 9 Developmental and Educational Psychology 0.0500000 0.017136981 2.917667
## 10 Experimental and Cognitive Psychology 0.0700000 0.021381930 3.273792
## 11 General Psychology 0.0400000 0.017741486 2.254603
## 12 Social Psychology 0.0400000 0.013269282 3.014481
## 13 Applied Psychology 0.1100000 0.037468584 2.935793
## 14 Clinical Psychology 0.1300000 0.037883166 3.431603
## 15 Developmental and Educational Psychology 0.1200000 0.034262319 3.502390
## 16 Experimental and Cognitive Psychology 0.2000000 0.039899378 5.012609
## 17 General Psychology 0.1200000 0.035470918 3.383053
## 18 Social Psychology 0.1200000 0.038910004 3.084040
## 19 Applied Psychology 0.2500000 0.065592306 3.811423
## 20 Clinical Psychology 0.3027526 0.081820996 3.700183
## 21 Developmental and Educational Psychology 0.2700000 0.059979435 4.501543
## 22 Experimental and Cognitive Psychology 0.3900000 0.067709448 5.759905
## 23 General Psychology 0.2600000 0.062095202 4.187119
## 24 Social Psychology 0.2700000 0.053077128 5.086937
## 25 Applied Psychology 0.4800000 0.132567686 3.620792
## 26 Clinical Psychology 0.5100000 0.110244841 4.626067
## 27 Developmental and Educational Psychology 0.4600000 0.103905934 4.427081
## 28 Experimental and Cognitive Psychology 0.6200000 0.086429438 7.173482
## 29 General Psychology 0.4500000 0.113547381 3.963103
## 30 Social Psychology 0.4300000 0.083434894 5.153719
## 31 Applied Psychology 0.6400000 0.194384749 3.292439
## 32 Clinical Psychology 0.6400000 0.103078098 6.208884
## 33 Developmental and Educational Psychology 0.5900000 0.123058273 4.794477
## 34 Experimental and Cognitive Psychology 0.7500000 0.125107207 5.994858
## 35 General Psychology 0.5900000 0.162787788 3.624350
## 36 Social Psychology 0.5200000 0.134104624 3.877570
## 37 Applied Psychology 0.9600000 0.134120683 7.157733
## 38 Clinical Psychology 0.8100000 0.246553996 3.285284
## 39 Developmental and Educational Psychology 0.8600000 0.235853256 3.646335
## 40 Experimental and Cognitive Psychology 0.9100000 0.078473532 11.596267
## 41 General Psychology 0.9200000 0.205105279 4.485501
## 42 Social Psychology 0.7300000 0.214277411 3.406799
## Pr(>|t|) percentile
## 1 0.113776158591470944 10
## 2 0.085400572552026599 10
## 3 0.083803257669567532 10
## 4 0.165601491916319121 10
## 5 0.094864177740357603 10
## 6 0.093953254050930024 10
## 7 0.033274655847866264 25
## 8 0.020608331613446218 25
## 9 0.003678274347020949 25
## 10 0.001131332200966062 25
## 11 0.024571159998310943 25
## 12 0.002699117627202208 25
## 13 0.003473351315522955 50
## 14 0.000647509540810276 50
## 15 0.000500573873442711 50
## 16 0.000000736192478756 50
## 17 0.000770568109081715 50
## 18 0.002149998777364992 50
## 19 0.000154612213986383 75
## 20 0.000238268180870804 75
## 21 0.000008322818373152 75
## 22 0.000000014373861656 75
## 23 0.000033149912034958 75
## 24 0.000000508032120816 75
## 25 0.000322294666398770 90
## 26 0.000004703788670790 90
## 27 0.000011633948709733 90
## 28 0.000000000002513767 90
## 29 0.000084260132959368 90
## 30 0.000000362616124461 90
## 31 0.001060347086306646 95
## 32 0.000000001088054535 95
## 33 0.000002129239898618 95
## 34 0.000000003798532155 95
## 35 0.000317997614870169 95
## 36 0.000118944747262706 95
## 37 0.000000000002791101 99
## 38 0.001087081101116993 99
## 39 0.000292620874845273 99
## 40 0.000000000000000000 99
## 41 0.000008949113199375 99
## 42 0.000707888231883391 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 1.060000 0.01657495 63.951923
## 2 Clinical Psychology 1.060000 0.01641569 64.572385
## 3 Developmental and Educational Psychology 1.100000 0.02506432 43.887083
## 4 Experimental and Cognitive Psychology 1.070000 0.02158799 49.564585
## 5 General Psychology 1.080000 0.02133674 50.616924
## 6 Social Psychology 1.080000 0.02060641 52.410883
## 7 Applied Psychology 1.250000 0.04510557 27.712762
## 8 Clinical Psychology 1.230000 0.04467216 27.533926
## 9 Developmental and Educational Psychology 1.300000 0.04464509 29.118543
## 10 Experimental and Cognitive Psychology 1.230000 0.04005519 30.707630
## 11 General Psychology 1.260000 0.04750680 26.522520
## 12 Social Psychology 1.300000 0.04970409 26.154788
## 13 Applied Psychology 1.710000 0.09509944 17.981179
## 14 Clinical Psychology 1.710000 0.09418564 18.155633
## 15 Developmental and Educational Psychology 1.820000 0.08925985 20.389907
## 16 Experimental and Cognitive Psychology 1.790000 0.13774305 12.995211
## 17 General Psychology 1.720000 0.09181528 18.733266
## 18 Social Psychology 1.790000 0.08561488 20.907581
## 19 Applied Psychology 2.610000 0.18862331 13.837102
## 20 Clinical Psychology 2.710000 0.23960523 11.310270
## 21 Developmental and Educational Psychology 2.910000 0.23066630 12.615627
## 22 Experimental and Cognitive Psychology 3.364052 0.41256849 8.153924
## 23 General Psychology 2.700000 0.23357509 11.559452
## 24 Social Psychology 2.800000 0.21028655 13.315165
## 25 Applied Psychology 4.650000 0.71272295 6.524274
## 26 Clinical Psychology 4.980000 0.82625623 6.027186
## 27 Developmental and Educational Psychology 5.560000 0.89730277 6.196348
## 28 Experimental and Cognitive Psychology 7.300000 1.40861676 5.182389
## 29 General Psychology 5.000000 0.85346952 5.858440
## 30 Social Psychology 5.180000 0.74183070 6.982725
## 31 Applied Psychology 7.100000 1.36102102 5.216672
## 32 Clinical Psychology 7.970000 1.80157797 4.423900
## 33 Developmental and Educational Psychology 9.450000 2.68345847 3.521575
## 34 Experimental and Cognitive Psychology 14.790000 5.61838745 2.632428
## 35 General Psychology 8.860000 2.48905629 3.559582
## 36 Social Psychology 8.400000 1.95847398 4.289054
## 37 Applied Psychology 23.830000 18.57373947 1.282994
## 38 Clinical Psychology 24.950000 20.20170639 1.235044
## 39 Developmental and Educational Psychology 39.330000 22.49678895 1.748249
## 40 Experimental and Cognitive Psychology 74.960000 41.27046987 1.816311
## 41 General Psychology 33.330000 32.69138521 1.019535
## 42 Social Psychology 23.490000 14.70010959 1.597947
## Pr(>|t|) percentile
## 1 0.0000000000000000000000 10
## 2 0.0000000000000000000000 10
## 3 0.0000000000000000000000 10
## 4 0.0000000000000000000000 10
## 5 0.0000000000000000000000 10
## 6 0.0000000000000000000000 10
## 7 0.0000000000000000000000 25
## 8 0.0000000000000000000000 25
## 9 0.0000000000000000000000 25
## 10 0.0000000000000000000000 25
## 11 0.0000000000000000000000 25
## 12 0.0000000000000000000000 25
## 13 0.0000000000000000000000 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000000000000000 75
## 20 0.0000000000000000000000 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000004440892 75
## 23 0.0000000000000000000000 75
## 24 0.0000000000000000000000 75
## 25 0.0000000000752327089515 90
## 26 0.0000000017905497085025 90
## 27 0.0000000006251994477680 90
## 28 0.0000002278580848447120 90
## 29 0.0000000049775841226563 90
## 30 0.0000000000032818192608 90
## 31 0.0000001896737353845879 95
## 32 0.0000099036951501929593 95
## 33 0.0004328996293869735723 95
## 34 0.0085043029282827475868 95
## 35 0.0003749683870624131998 95
## 36 0.0000182889808431063727 95
## 37 0.1995548008565468656172 99
## 38 0.2168732884834621010839 99
## 39 0.0804834710748378867606 99
## 40 0.0693838005129014945283 99
## 41 0.3079995559645656300063 99
## 42 0.1101192036667897333757 99
##
## $plot
#quantile_regression_and_plot(data_effectsizes_possible, "chi2", "chi2")
#quantile_regression_and_plot(data_effectsizes_possible, "B", "B")
#quantile_regression_and_plot(data_effectsizes_possible, "stdB", "")
quantile_regression_and_plot(data_effectsizes_possible, "abs_stdB", "Absolute std. B") ## $res
## subfield estimate se t value
## 1 Applied Psychology 0.05 0.02696946 1.8539486
## 2 Clinical Psychology 0.04 0.01795737 2.2274972
## 3 Developmental and Educational Psychology 0.05 0.01885801 2.6513930
## 4 Experimental and Cognitive Psychology 0.04 0.01610431 2.4838068
## 5 General Psychology 0.06 0.01893733 3.1683450
## 6 Social Psychology 0.05 0.01927288 2.5943187
## 7 Applied Psychology 0.13 0.02668809 4.8710862
## 8 Clinical Psychology 0.11 0.02665504 4.1267995
## 9 Developmental and Educational Psychology 0.11 0.02799190 3.9297081
## 10 Experimental and Cognitive Psychology 0.13 0.02390445 5.4383189
## 11 General Psychology 0.12 0.02108223 5.6919976
## 12 Social Psychology 0.11 0.02145579 5.1268221
## 13 Applied Psychology 0.24 0.03735064 6.4255932
## 14 Clinical Psychology 0.23 0.03730438 6.1654963
## 15 Developmental and Educational Psychology 0.22 0.03357887 6.5517392
## 16 Experimental and Cognitive Psychology 0.28 0.03823412 7.3233016
## 17 General Psychology 0.23 0.02810009 8.1850272
## 18 Social Psychology 0.21 0.03431760 6.1193097
## 19 Applied Psychology 0.45 0.06672023 6.7445808
## 20 Clinical Psychology 0.44 0.07996512 5.5023993
## 21 Developmental and Educational Psychology 0.40 0.06997975 5.7159390
## 22 Experimental and Cognitive Psychology 0.63 0.14940279 4.2167887
## 23 General Psychology 0.40 0.05621928 7.1149970
## 24 Social Psychology 0.39 0.06436736 6.0589715
## 25 Applied Psychology 0.83 0.30565393 2.7154894
## 26 Clinical Psychology 1.08 0.54769992 1.9718827
## 27 Developmental and Educational Psychology 0.83 0.34887323 2.3790877
## 28 Experimental and Cognitive Psychology 2.11 0.90184155 2.3396571
## 29 General Psychology 0.64 0.11362399 5.6326132
## 30 Social Psychology 0.73 0.25054749 2.9136193
## 31 Applied Psychology 1.80 1.24568259 1.4449909
## 32 Clinical Psychology 2.59 1.59505098 1.6237726
## 33 Developmental and Educational Psychology 1.91 1.28420460 1.4873019
## 34 Experimental and Cognitive Psychology 5.80 4.74910780 1.2212820
## 35 General Psychology 0.90 0.47098660 1.9108824
## 36 Social Psychology 1.48 0.97007674 1.5256525
## 37 Applied Psychology 15.81 28.85536527 0.5479050
## 38 Clinical Psychology 15.18 27.83647240 0.5453277
## 39 Developmental and Educational Psychology 14.42 30.15718261 0.4781614
## 40 Experimental and Cognitive Psychology 49.46 40.74245504 1.2139671
## 41 General Psychology 7.16 18.91010873 0.3786335
## 42 Social Psychology 9.55 18.80420821 0.5078650
## Pr(>|t|) percentile
## 1 0.0638032445341996101718 10
## 2 0.0259568008917718806572 10
## 3 0.0080404245957799158617 10
## 4 0.0130299732420353642226 10
## 5 0.0015419993085714622794 10
## 6 0.0095045245482150431116 10
## 7 0.0000011428626613962933 25
## 8 0.0000373613414139661870 25
## 9 0.0000861555265028179917 25
## 10 0.0000000562502455725422 25
## 11 0.0000000132456414725368 25
## 12 0.0000003053763144578170 25
## 13 0.0000000001431033069821 50
## 14 0.0000000007557725556495 50
## 15 0.0000000000623761042817 50
## 16 0.0000000000002791100684 50
## 17 0.0000000000000004440892 50
## 18 0.0000000010087828350436 50
## 19 0.0000000000170201630567 75
## 20 0.0000000392639991630972 75
## 21 0.0000000115191736149711 75
## 22 0.0000252025048994575229 75
## 23 0.0000000000012714274078 75
## 24 0.0000000014665453296203 75
## 25 0.0066397105391351463055 90
## 26 0.0486760508661348012538 90
## 27 0.0173915161306288368337 90
## 28 0.0193391882706013262805 90
## 29 0.0000000186847628480535 90
## 30 0.0035879174646997746834 90
## 31 0.1485208511558906430139 95
## 32 0.1044851231820742576417 95
## 33 0.1369958652283056466104 95
## 34 0.2220347618924325416856 95
## 35 0.0560747657441984515003 95
## 36 0.1271573984650182786993 95
## 37 0.5837806858853216152028 99
## 38 0.5855515908649571166222 99
## 39 0.6325555002520606429073 99
## 40 0.2248155570196779251546 99
## 41 0.7049755637107022465671 99
## 42 0.6115695864430894523878 99
##
## $plot
## R version 4.5.0 (2025-04-11)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggstance_0.3.7 quantreg_6.1 SparseM_1.84-2 kableExtra_1.4.0
## [5] knitr_1.50 ggridges_0.5.6 patchwork_1.3.0 rlang_1.1.6
## [9] janitor_2.2.1 scales_1.4.0 lubridate_1.9.4 forcats_1.0.0
## [13] stringr_1.5.1 dplyr_1.1.4 purrr_1.1.0 readr_2.1.5
## [17] tidyr_1.3.1 tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.6 sass_0.4.10 generics_0.1.4 xml2_1.3.8
## [5] lattice_0.22-6 stringi_1.8.7 hms_1.1.3 digest_0.6.37
## [9] magrittr_2.0.3 evaluate_1.0.3 grid_4.5.0 timechange_0.3.0
## [13] RColorBrewer_1.1-3 fastmap_1.2.0 Matrix_1.7-3 jsonlite_2.0.0
## [17] survival_3.8-3 viridisLite_0.4.2 textshaping_1.0.1 jquerylib_0.1.4
## [21] cli_3.6.5 splines_4.5.0 withr_3.0.2 cachem_1.1.0
## [25] yaml_2.3.10 tools_4.5.0 MatrixModels_0.5-4 tzdb_0.5.0
## [29] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4 snakecase_0.11.1
## [33] MASS_7.3-65 ragg_1.4.0 pkgconfig_2.0.3 pillar_1.11.0
## [37] bslib_0.9.0 gtable_0.3.6 glue_1.8.0 systemfonts_1.2.3
## [41] xfun_0.52 tidyselect_1.2.1 rstudioapi_0.17.1 farver_2.1.2
## [45] htmltools_0.5.8.1 rmarkdown_2.29 svglite_2.2.1 compiler_4.5.0